Optimal smoothing in discrete-continuous linear and nonlinear systems

Abstract A receiver observes discrete noisy nonlinear samples of a continuous Gauss-Markov process. It is shown that a stable smoother can be realized as a cascade of two blocks: 1. (1) a discrete smoother producing discrete smoothed estimates and 2. (2) a continuous element reconstructing a continuous estimate. A remarkable invariance formula is shown to exist for the latter part. A few linear examples are considered illustrating the error variance dependence on important model parameters. A major point is that an analysis of the reconstruction problem is of importance not only for low sample rates but also for high input SNR.

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